scMaSigPro: Differential Expression Analysis along Single-Cell Trajectories.

MOTIVATION: Understanding the dynamics of gene expression across different cellular states is crucial for discerning the mechanisms underneath cellular differentiation. Genes that exhibit variation in mean expression as a function of Pseudotime and between branching trajectories are expected to govern cell fate decisions. We introduce scMaSigPro, a method for the identification of differential gene expression patterns along Pseudotime and branching paths simultaneously. RESULTS: We assessed the performance of scMaSigPro using synthetic and public datasets. Our evaluation shows that scMaSigPro outperforms existing methods in controlling the False Positive Rate and is computationally efficient. AVAILABILITY AND IMPLEMENTATION: scMaSigPro is available as a free R package (version 4.0 or higher) under the GPL(≥2) license on GitHub at 'github.com/BioBam/scMaSigPro' and archived with version 0.03 on Zenodo at 'zenodo.org/records/12568922'.

Causal relationship between immune cells and hepatocellular carcinoma: a Mendelian randomisation study.

Background: Hepatocellular carcinoma (HCC), the predominant malignancy of the digestive tract, ranks as the third most common cause of cancer-related mortality globally, significantly impeding human health and lifespan. Emerging immunotherapeutic approaches have ignited fresh optimism for patient outcomes. This investigation probes the link between 731 immune cell phenotypes and HCC through Mendelian Randomization and single-cell sequencing, aiming to unearth viable drug targets and dissect HCC's etiology. Methods: We conducted an exhaustive two-sample Mendelian Randomization analysis to ascertain the causal links between immune cell features and HCC, utilizing publicly accessible genetic datasets to explore the causal connections of 731 immune cell traits with HCC susceptibility. The integrity, diversity, and potential horizontal pleiotropy of these findings were rigorously assessed through extensive sensitivity analyses. Furthermore, single-cell sequencing was employed to penetrate the pathogenic underpinnings of HCC. Results: Establishing a significance threshold of pval_Inverse.variance.weighted at 0.05, our study pinpointed five immune characteristics potentially elevating HCC risk: B cell % CD3- lymphocyte (TBNK panel), CD25 on IgD+ (B cell panel), HVEM on TD CD4+ (Maturation stages of T cell panel), CD14 on CD14+ CD16- monocyte (Monocyte panel), CD4 on CD39+ activated Treg ( Treg panel). Conversely, various cellular phenotypes tied to BAFF-R expression emerged as protective elements. Single-cell sequencing unveiled profound immune cell phenotype interactions, highlighting marked disparities in cell communication and metabolic activities. Conclusion: Leveraging MR and scRNA-seq techniques, our study elucidates potential associations between 731 immune cell phenotypes and HCC, offering a window into the molecular interplays among cellular phenotypes, and addressing the limitations of mono-antibody therapeutic targets.

Inferring Tree-Shaped Single-Cell Trajectories with Totem.

Single-cell transcriptomics allows unbiased characterization of cell heterogeneity in a sample by profiling gene expression at single-cell level. These profiles capture snapshots of transient or steady states in dynamic processes, such as cell cycle, activation, or differentiation, which can be computationally ordered into a "flip-book" of cell development using trajectory inference methods. However, prediction of more complex topology structures, such as multifurcations or trees, remains challenging. In this chapter, we present two user-friendly protocols for inferring tree-shaped single-cell trajectories and pseudotime from single-cell transcriptomics data with Totem. Totem is a trajectory inference method that offers flexibility in inferring both nonlinear and linear trajectories and usability by avoiding the cumbersome fine-tuning of parameters. The QuickStart protocol provides a simple and practical example, whereas the GuidedStart protocol details the analysis step-by-step. Both protocols are demonstrated using a case study of human bone marrow CD34+ cells, allowing the study of the branching of three lineages: erythroid, lymphoid, and myeloid. All the analyses can be fully reproduced in Linux, macOS, and Windows operating systems (amd64 architecture) with >8 Gb of RAM using the provided docker image distributed with notebooks, scripts, and data in Docker Hub (elolab/repro-totem-ti). These materials are shared online under open-source license at https://elolab.github.io/Totem-protocol .

A spatial portrait of the human sebaceous gland transcriptional program.

Sebaceous glands (SG) and their oily secretion (sebum) are indispensable for maintaining skin structure and function, and their deregulation causes skin disorders including but not limited to acne. Recent studies also indicate that sebum may have important immunomodulatory activities and may influence whole-body energy metabolism. However, the progressive transcriptional changes of sebocytes that lead to sebum production have never been characterized in detail. Here, we exploited the high cellular resolution provided by sebaceous hyperplasia and integrated spatial transcriptomics, pseudo time analysis, RNA velocity, and functional enrichment to map the landscape of sebaceous differentiation. Our results were validated by comparison with published SG transcriptome data and further corroborated by assessing the protein expression pattern of a subset of the transcripts in the public repository Human Protein Atlas. Departing from four sebocyte differentiation stages generated by unsupervised clustering, we demonstrate consecutive modulation of cellular functions associable with specific gene sets, from cell proliferation and oxidative phosphorylation via lipid synthesis to cell death. Both validation methods confirmed the biological significance of our results. Our report is complemented by a freely available and browsable online tool. Our data provide the first high-resolution spatial portrait of the SG transcriptional landscape and deliver starting points for experimentally assessing novel candidate molecules for regulating SG homeostasis in health and disease.

Identifying Key Regulatory Genes in Drug Resistance Acquisition: Modeling Pseudotime Trajectories of Breast Cancer Single-Cell Transcriptome.

Single-cell RNA-sequencing (scRNA-seq) technology has provided significant insights into cancer drug resistance at the single-cell level. However, understanding dynamic cell transitions at the molecular systems level remains limited, requiring a systems biology approach. We present an approach that combines mathematical modeling with a pseudotime analysis using time-series scRNA-seq data obtained from the breast cancer cell line MCF-7 treated with tamoxifen. Our single-cell analysis identified five distinct subpopulations, including tamoxifen-sensitive and -resistant groups. Using a single-gene mathematical model, we discovered approximately 560-680 genes out of 6000 exhibiting multistable expression states in each subpopulation, including key estrogen-receptor-positive breast cancer cell survival genes, such as RPS6KB1. A bifurcation analysis elucidated their regulatory mechanisms, and we mapped these genes into a molecular network associated with cell survival and metastasis-related pathways. Our modeling approach comprehensively identifies key regulatory genes for drug resistance acquisition, enhancing our understanding of potential drug targets in breast cancer.

Investigating the basis of lineage decisions and developmental trajectories in the dorsal spinal cord through pseudotime analyses.

Dorsal interneurons (dIs) in the spinal cord encode the perception of touch, pain, heat, itchiness and proprioception. Previous studies using genetic strategies in animal models have revealed important insights into dI development, but the molecular details of how dIs arise as distinct populations of neurons remain incomplete. We have developed a resource to investigate dI fate specification by combining a single-cell RNA-Seq atlas of mouse embryonic stem cell-derived dIs with pseudotime analyses. To validate this in silico resource as a useful tool, we used it to first identify genes that are candidates for directing the transition states that lead to distinct dI lineage trajectories, and then validated them using in situ hybridization analyses in the developing mouse spinal cord in vivo. We have also identified an endpoint of the dI5 lineage trajectory and found that dIs become more transcriptionally homogeneous during terminal differentiation. This study introduces a valuable tool for further discovery about the timing of gene expression during dI differentiation and demonstrates its utility in clarifying dI lineage relationships.

Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference.

Single-cell RNA sequencing (scRNA-seq) experiments have become instrumental in developmental and differentiation studies, enabling the profiling of cells at a single or multiple time-points to uncover subtle variations in expression profiles reflecting underlying biological processes. Benchmarking studies have compared many of the computational methods used to reconstruct cellular dynamics; however, researchers still encounter challenges in their analysis due to uncertainty with respect to selecting the most appropriate methods and parameters. Even among universal data processing steps used by trajectory inference methods such as feature selection and dimension reduction, trajectory methods' performances are highly dataset-specific. To address these challenges, we developed Escort, a novel framework for evaluating a dataset's suitability for trajectory inference and quantifying trajectory properties influenced by analysis decisions. Escort evaluates the suitability of trajectory analysis and the combined effects of processing choices using trajectory-specific metrics. Escort navigates single-cell trajectory analysis through these data-driven assessments, reducing uncertainty and much of the decision burden inherent to trajectory inference analyses. Escort is implemented in an accessible R package and R/Shiny application, providing researchers with the necessary tools to make informed decisions during trajectory analysis and enabling new insights into dynamic biological processes at single-cell resolution.

Construction of a pathological model of skin lesions in acute herpes zoster virus infection and its molecular mechanism.

Varicella-zoster virus (VZV), a common pathogen with humans as the sole host, causes primary infection and undergoes a latent period in sensory ganglia. The recurrence of VZV is often accompanied by severe neuralgia in skin tissue, which has a serious impact on the life of patients. During the acute infection of VZV, there are few related studies on the pathophysiological mechanism of skin tissue. In this study, transcriptome sequencing data from the acute response period within 2 days of VZV antigen stimulation of the skin were used to explore a model of the trajectory of skin tissue changes during VZV infection. It was found that early VZV antigen stimulation caused activation of mainly natural immune-related signaling pathways, while in the late phase activation of mainly active immune-related signaling pathways. JAK-STAT, NFκB, and TNFα signaling pathways are gradually activated with the progression of infection, while Hypoxia is progressively inhibited. In addition, we found that dendritic cell-mediated immune responses play a dominant role in the lesion damage caused by VZV antigen stimulation of the skin. This study provides a theoretical basis for the study of the molecular mechanisms of skin lesions during acute VZV infection.

[Dynamic trajectory and cell communication of different cell clusters in malignant progression of glioblastoma].

OBJECTIVE: To delve deeply into the dynamic trajectories of cell subpopulations and the communication network among immune cell subgroups during the malignant progression of glioblastoma (GBM), and to endeavor to unearth key risk biomarkers in the GBM malignancy progression, so as to provide a more profound understanding for the treatment and prognosis of this disease by integrating transcriptomic data and clinical information of the GBM patients. METHODS: Utilizing single-cell sequencing data analysis, we constructed a cell subgroup atlas during the malignant progression of GBM. The Monocle2 tool was employed to build dynamic progression trajectories of the tumor cell subgroups in GBM. Through gene enrichment analysis, we explored the biological processes enriched in genes that significantly changed with the malignancy progression of GBM tumor cell subpopulations. CellChat was used to identify the communication network between the different immune cell subgroups. Survival analysis helped in identifying risk molecular markers that impacted the patient prognosis during the malignant progression of GBM. This method ological approach offered a comprehensive and detailed examination of the cellular and molecular dynamics within GBM, providing a robust framework for understanding the disease' s progression and potential therapeutic targets. RESULTS: The analysis of single-cell sequencing data identified 6 different cell types, including lymphocytes, pericytes, oligodendrocytes, macrophages, glioma cells, and microglia. The 27 151 cells in the single-cell dataset included 3 881 cells from the patients with low-grade glioma (LGG), 10 166 cells from the patients with newly diagnosed GBM, and 13 104 cells from the patients with recurrent glioma (rGBM). The pseudo-time analysis of the glioma cell subgroups indicated significant cellular heterogeneity during malignant progression. The cell interaction analysis of immune cell subgroups revealed the communication network among the different immune subgroups in GBM malignancy, identifying 22 biologically significant ligand-receptor pairs across 12 key biological pathways. Survival analysis had identified 8 genes related to the prognosis of the GBM patients, among which SERPINE1, COL6A1, SPP1, LTF, C1S, AEBP1, and SAA1L were high-risk genes in the GBM patients, and ABCC8 was low-risk genes in the GBM patients. These findings not only provided new theoretical bases for the treatment of GBM, but also offered fresh insights for the prognosis assessment and treatment decision-making for the GBM patients. CONCLUSION: This research comprehensively and profoundly reveals the dynamic changes in glioma cell subpopulations and the communication patterns among the immune cell subgroups during the malignant progression of GBM. These findings are of significant importance for understanding the complex biological processes of GBM, providing crucial new insights for precision medicine and treatment decisions in GBM. Through these studies, we hope to provide more effective treatment options and more accurate prognostic assessments for the patients with GBM.

Single-Cell Nuclear Sequencing Atlas Revealed the Induction of Parkinson's Disease by RELN+ Neuron 3 and the Gene Regulatory Network of MSRA.

AIMS: To determine the cell types that promoted the progression of Parkinson's disease (PD) using the substantia nigra in the brain tissues derived from patients with PD and normal controls. BACKGROUND: PD is an incurable neurodegenerative disease that threatens the physical activity of the aging population, and the complex molecular mechanisms remain be comprehensively elucidated. OBJECTIVE: To describe potential disease-promoting cell types in PD and to provide a theoretical basis. METHODS: Single-cell nuclear sequencing data of nine PD samples and control samples from Gene Expression Omnibus (GEO) were included, and heterogeneous cell subpopulations in the substantia nigra were identified by annotation analysis. Potential pathogenic cell subpopulations of PD were determined based on the expression data of marker genes. Cell differentiation trajectories and communication networks were generated by Pseudotime trajectory analysis and cell communication analysis. Furthermore, single-- cell regulatory network inference and clustering (SCENIC) analysis was conducted to determine the regulatory network of transcription factor-target genes in PD. RESULTS: Among the nine cell subpopulations classified, RELN+neuron 3 showed reduced abundance and dopamine secretion capacity in PD and was therefore considered as a promoter of PD pathogenesis and progression. The regulatory network of MSRA action was involved in the developmental process of cells in the central nervous system, indicating that MSRA and its targets might serve as potential therapeutic targets for PD. RELN+neuron 3 had two directions of differentiation, specifically, branch 1 exhibited a high apoptotic profile and branch 2 exhibited a high cell death profile. In addition, the intensity of EPHA and EPHB signaling was attenuated between RELN+neuron 3 and other cell subpopulations. CONCLUSION: To conclude, this study identified a subpopulation of RELN+neuron 3 cells with markedly reduced abundance in the brain substantia nigra in PD. The MSRA-involved gene regulatory networks was considered as a novel therapeutic network for PD.

Single-Cell RNA-Sequencing Reveals Heterogeneity and Transcriptional Dynamics in Porcine Circulating CD8+ T Cells.

Pigs are the most important source of meat and valuable biomedical models. However, the porcine immune system, especially the heterogeneity of CD8 T cell subtypes, has not been fully characterized. Here, using single-cell RNA sequencing, we identified 14 major cell types from peripheral blood circulating cells of pigs and observed remarkable heterogeneity among CD8 T cell types. Upon re-clustering of CD8+ T cells, we defined four CD8 T cell subtypes and revealed their potential differentiation trajectories and transcriptomic differences among them. Additionally, we identified transcription factors with potential regulatory roles in maintaining CD8 T cell differentiation. The cell-cell communication analysis inferred an extensive interaction between CD8 T cells and other immune cells. Finally, cross-species analysis further identified species-specific and conserved cell types across different species. Overall, our study provides the first insight into the extensive functional heterogeneity and state transitions among porcine CD8 T cell subtypes in pig peripheral blood, complements the knowledge of porcine immunity, and enhances its potential as a biomedical model.

STIGMA: Single-cell tissue-specific gene prioritization using machine learning.

Clinical exome and genome sequencing have revolutionized the understanding of human disease genetics. Yet many genes remain functionally uncharacterized, complicating the establishment of causal disease links for genetic variants. While several scoring methods have been devised to prioritize these candidate genes, these methods fall short of capturing the expression heterogeneity across cell subpopulations within tissues. Here, we introduce single-cell tissue-specific gene prioritization using machine learning (STIGMA), an approach that leverages single-cell RNA-seq (scRNA-seq) data to prioritize candidate genes associated with rare congenital diseases. STIGMA prioritizes genes by learning the temporal dynamics of gene expression across cell types during healthy organogenesis. To assess the efficacy of our framework, we applied STIGMA to mouse limb and human fetal heart scRNA-seq datasets. In a cohort of individuals with congenital limb malformation, STIGMA prioritized 469 variants in 345 genes, with UBA2 as a notable example. For congenital heart defects, we detected 34 genes harboring nonsynonymous de novo variants (nsDNVs) in two or more individuals from a set of 7,958 individuals, including the ortholog of Prdm1, which is associated with hypoplastic left ventricle and hypoplastic aortic arch. Overall, our findings demonstrate that STIGMA effectively prioritizes tissue-specific candidate genes by utilizing single-cell transcriptome data. The ability to capture the heterogeneity of gene expression across cell populations makes STIGMA a powerful tool for the discovery of disease-associated genes and facilitates the identification of causal variants underlying human genetic disorders.

Conduction and validation of a novel prognostic signature in cervical cancer based on the necroptosis characteristic genes via integrating of multiomics data.

The significance of necroptosis in recurrent or metastatic cervical cancer remains unclear. In this study, we utilized various bioinformatics methods to analyze the cancer genome atlas (TCGA) data, gene chip and the single-cell RNA-sequencing (scRNA seq) data. And a necroptosis-related genes signature for prognostic assessment of patients with cervical cancer was constructed successfully. Survival analysis, receiver operating characteristic (ROC) curve, the support vector machine recursive feature elimination (SVM-RFE) algorithm and random forest analysis were performed to validate this signature. Patients in TCGA-CESC cohort were grouped into "high-necroptosis score (H-NCPS)" vs "low-necroptosis score (L-NCPS)" subgroups based on the median of necroptosis score of each patient. Analyses of the tumor microenvironment manifested "H-NCPS" patients associated with lower degree of immune infiltration. Through the utilization of survival analysis, cell communication, and Gene Set Enrichment Analysis (GSEA), PGK1 was determined to be the pivotal gene within the 9-gene signature associated with necroptosis. The high expression of PGK1 in cervical cancer cells was confirmed through the utilization of quantitative real-time polymerase chain reaction (RT-qPCR) and the human protein atlas (HPA). In the interim, PGK1 prompted the transition of M1 macrophages to M2 macrophages and influenced the occurrence and development of necroptosis. In conclusion, the 9-gene signature developed from necroptosis-related genes has shown significant predictive capabilities for the prognosis of cervical cancer, offered valuable guidance for individualized and targeted treatment approaches for patients.

Revealing the role of necroptosis microenvironment: FCGBP + tumor-associated macrophages drive primary liver cancer differentiation towards cHCC-CCA or iCCA.

Previous research has demonstrated that the conversion of hepatocellular carcinoma (HCC) to intrahepatic cholangiocarcinoma (iCCA) can be stimulated by manipulating the tumor microenvironment linked with necroptosis. However, the specific cells regulating the necroptosis microenvironment have not yet been identified. Additionally, further inquiry into the mechanism of how the tumor microenvironment regulates necroptosis and its impact on primary liver cancer(PLC) progression may be beneficial for precision therapy. We recruited a single-cell RNA sequencing dataset (scRNA-seq) with 34 samples from 4 HCC patients and 3 iCCA patients, and a Spatial Transcriptomic (ST) dataset including one each of HCC, iCCA, and combined hepatocellular-cholangiocarcinoma (cHCC-CCA). Quality control, dimensionality reduction and clustering were based on Seurat software (v4.2.2) process and batch effects were removed by harmony (v0.1.1) software. The pseudotime analysis (also known as cell trajectory) in the single cell dataset was performed by monocle2 software (v2.24.0). Calculation of necroptosis fraction was performed by AUCell (v1.16.0) software. Switch gene analysis was performed by geneSwitches(v0.1.0) software. Dimensionality reduction, clustering, and spatial image in ST dataset were performed by Seurat (v4.0.2). Tumor cell identification, tumor subtype characterization, and cell type deconvolution in spot were performed by SpaCET (v1.0.0) software. Immunofluorescence and immunohistochemistry experiments were used to prove our conclusions. Analysis of intercellular communication was performed using CellChat software (v1.4.0). ScRNA-seq analysis of HCC and iCCA revealed that necroptosis predominantly occurred in the myeloid cell subset, particularly in FCGBP + SPP1 + tumor-associated macrophages (TAMs), which had the highest likelihood of undergoing necroptosis. The existence of macrophages undergoing necroptosis cell death was further confirmed by immunofluorescence. Regions of HCC with poor differentiation, cHCC-CCA with more cholangiocarcinoma features, and the tumor region of iCCA shared spatial colocalization with FCGBP + macrophages, as confirmed by spatial transcriptomics, immunohistochemistry and immunofluorescence. Pseudotime analysis showed that premalignant cells could progress into two directions, one towards HCC and the other towards iCCA and cHCC-CCA. Immunofluorescence and immunohistochemistry experiments demonstrated that the number of macrophages undergoing necroptosis in cHCC-CCA was higher than in iCCA and HCC, the number of macrophages undergoing necroptosis in cHCC-CCA with cholangiocarcinoma features was more than in cHCC-CCA with hepatocellular carcinoma features. Further investigation showed that myeloid cells with the highest necroptosis score were derived from the HCC_4 case, which had a severe inflammatory background on pathological histology and was likely to progress towards iCCA and cHCC-CCA. Switchgene analysis indicated that S100A6 may play a significant role in the progression of premalignant cells towards iCCA and cHCC-CCA. Immunohistochemistry confirmed the expression of S100A6 in PLC, the more severe inflammatory background of the tumor area, the more cholangiocellular carcinoma features of the tumor area, S100A6 expression was higher. The emergence of necroptosis microenvironment was found to be significantly associated with FCGBP + SPP1 + TAMs in PLC. In the presence of necroptosis microenvironment, premalignant cells appeared to transform into iCCA or cHCC-CCA. In contrast, without a necroptosis microenvironment, premalignant cells tended to develop into HCC, exhibiting amplified stemness-related genes (SRGs) and heightened malignancy.

Unravelling human hematopoietic progenitor cell diversity through association with intrinsic regulatory factors.

Hematopoietic stem and progenitor cell (HSPC) transplantation is an essential therapy for hematological conditions, but finer definitions of human HSPC subsets with associated function could enable better tuning of grafts and more routine, lower-risk application. To deeply phenotype HSPCs, following a screen of 328 antigens, we quantified 41 surface proteins and functional regulators on millions of CD34+ and CD34- cells, spanning four primary human hematopoietic tissues: bone marrow, mobilized peripheral blood, cord blood, and fetal liver. We propose more granular definitions of HSPC subsets and provide new, detailed differentiation trajectories of erythroid and myeloid lineages. These aspects of our revised human hematopoietic model were validated with corresponding epigenetic analysis and in vitro clonal differentiation assays. Overall, we demonstrate the utility of using molecular regulators as surrogates for cellular identity and functional potential, providing a framework for description, prospective isolation, and cross-tissue comparison of HSPCs in humans.

LVPT: Lazy Velocity Pseudotime Inference Method.

The emergence of RNA velocity has enriched our understanding of the dynamic transcriptional landscape within individual cells. In light of this breakthrough, we embarked on integrating RNA velocity with cellular pseudotime inference, aiming to improve the prediction of cell orders along biological trajectories beyond existing methods. Here, we developed LVPT, a novel method for pseudotime and trajectory inference. LVPT introduces a lazy probability to indicate the probability that the cell stays in the original state and calculates the transition matrix based on RNA velocity to provide the probability and direction of cell differentiation. LVPT shows better and comparable performance of pseudotime inference compared with other existing methods on both simulated datasets with different structures and real datasets. The validation results were consistent with prior knowledge, indicating that LVPT is an accurate and efficient method for pseudotime inference.

Integrating the characteristic genes of macrophage pseudotime analysis in single-cell RNA-seq to construct a prediction model of atherosclerosis.

BACKGROUND: Macrophages play an important role in the occurrence and development of atherosclerosis. However, few existing studies have deliberately analyzed the changes in characteristic genes in the process of macrophage phenotype transformation. METHOD: Carotid atherosclerotic plaque single-cell RNA (scRNA) sequencing data were analyzed to define the cells involved and determine their transcriptomic characteristics. KEGG enrichment analysis, CIBERSORT, ESTIMATE, support vector machine (SVM), random forest (RF), and weighted correlation network analysis (WGCNA) were applied to bulk sequencing data. All data were downloaded from Gene Expression Omnibus (GEO). RESULT: Nine cell clusters were identified. M1 macrophages, M2 macrophages, and M2/M1 macrophages were identified as three clusters within the macrophages. According to pseudotime analysis, M2/M1 macrophages and M2 macrophages can be transformed into M1 macrophages. The ROC curve values of the six genes in the test group were statistically significant (AUC (IL1RN): 0.899, 95% CI: 0.764-0.990; AUC (NRP1): 0.817, 95% CI: 0.620-0.971; AUC (TAGLN): 0.846, 95% CI: 0.678-0.971; AUC (SPARCL1): 0.825, 95% CI: 0.620-0.988; AUC (EMP2): 0.808, 95% CI: 0.630-0.947; AUC (ACTA2): 0.784, 95% CI: 0.591-0.938). The atherosclerosis prediction model showed significant statistical significance in both the train group (AUC: 0.909, 95% CI: 0.842-0.967) and the test group (AUC: 0.812, 95% CI: 0.630-0.966). CONCLUSIONS: IL1RNHigh M1, NRP1High M2, ACTA2High M2/M1, EMP2High M1/M1, SPACL1High M2/M1 and TAGLNHigh M2/M1 macrophages play key roles in the occurrence and development of arterial atherosclerosis. These marker genes of macrophage phenotypic transformation can also be used to establish a model to predict the occurrence of atherosclerosis.

Integrative analysis of single-cell embryo data reveals transcriptome signatures for the human pre-implantation inner cell mass.

As the source of embryonic stem cells (ESCs), inner cell mass (ICM) can form all tissues of the embryo proper, however, its role in early human lineage specification remains controversial. Although a stepwise differentiation model has been proposed suggesting the existence of ICM as a distinct developmental stage, the underlying molecular mechanism remains unclear. In the present study, we perform an integrated analysis on the public human preimplantation embryonic single-cell transcriptomic data and apply a trajectory inference algorithm to measure the cell plasticity. In our results, ICM population can be clearly discriminated on the dimension-reduced graph and confirmed by compelling evidences, thus validating the two-step hypothesis of lineage commitment. According to the branch probabilities and differentiation potential, we determine the precise time points for two lineage segregations. Further analysis on gene expression dynamics and regulatory network indicates that transcription factors including GSC, PRDM1, and SPIC may underlie the decisions of ICM fate. In addition, new human ICM marker genes, such as EPHA4 and CCR8 are discovered and validated by immunofluorescence. Given the potential clinical applications of ESCs, our analysis provides a further understanding of human ICM cells and facilitates the exploration of more unique characteristics in early human development.

scTour: a deep learning architecture for robust inference and accurate prediction of cellular dynamics.

Despite the continued efforts, a batch-insensitive tool that can both infer and predict the developmental dynamics using single-cell genomics is lacking. Here, I present scTour, a novel deep learning architecture to perform robust inference and accurate prediction of cellular dynamics with minimal influence from batch effects. For inference, scTour simultaneously estimates the developmental pseudotime, delineates the vector field, and maps the transcriptomic latent space under a single, integrated framework. For prediction, scTour precisely reconstructs the underlying dynamics of unseen cellular states or a new independent dataset. scTour's functionalities are demonstrated in a variety of biological processes from 19 datasets.

Aging disrupts gene expression timing during muscle regeneration.

Skeletal muscle function and regenerative capacity decline during aging, yet factors driving these changes are incompletely understood. Muscle regeneration requires temporally coordinated transcriptional programs to drive myogenic stem cells to activate, proliferate, fuse to form myofibers, and to mature as myonuclei, restoring muscle function after injury. We assessed global changes in myogenic transcription programs distinguishing muscle regeneration in aged mice from young mice by comparing pseudotime trajectories from single-nucleus RNA sequencing of myogenic nuclei. Aging-specific differences in coordinating myogenic transcription programs necessary for restoring muscle function occur following muscle injury, likely contributing to compromised regeneration in aged mice. Differences in pseudotime alignment of myogenic nuclei when comparing aged with young mice via dynamic time warping revealed pseudotemporal differences becoming progressively more severe as regeneration proceeds. Disruptions in timing of myogenic gene expression programs may contribute to incomplete skeletal muscle regeneration and declines in muscle function as organisms age.